人工内分泌系统新机制及应用研究
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摘要
自然计算研究从自然界的信息组织与处理过程中提取出计算方法与模型,用于解决传统计算方法难以解决的计算问题。自然计算已经成为人工智能研究的一个重要方向,而从人体中提取计算方法则是自然计算领域的重要研究方向。人体内的信息控制系统主要包括神经系统、免疫系统、遗传系统和内分泌系统。这些控制系统体现了高度的智能性和自适应性,蕴含着丰富的有启发性的信息处理机制。
在人体的四个主要控制系统中,内分泌系统对人体的生长发育、新陈代谢等功能起到重要的调节作用。内分泌系统与神经系统、免疫系统紧密联系、相互合作,一起维持着机体的内平衡。内分泌系统通过一种称为激素的物质对机体进行调节,激素扩散到全身,对机体进行分布式的调节。内分泌系统与神经系统存在紧密的相互联系,它们互相调节互相作用。神经系统通过神经内分泌细胞调节内分泌系统的活动；内分泌系统则通过激素调节神经系统的活动。内分泌系统分泌的一些激素可以影响人的情感,从高层对神经网络进行调节。神经系统和内分泌系统共同组成一个负反馈调节系统,维持机体内环境的稳态。内分泌系统内部存在着复杂的反馈调节关系,以保证其对机体的调节处于可控状态。目前,对内分泌系统生理机制的研究还不充分,很多内分泌系统的信息作用机制仍未得到充分理解。由此可见,人体的内分泌系统蕴含丰富的信息处理机制,可以给自然计算研究带来新的启发,是自然计算的重要研究方向。受内分泌系统启发提出的自然计算模型称为人工内分泌系统。目前,对内分泌系统生理机制的建模已经蓬勃开展,但是该领域的研究仍不充分,应用研究还不够广泛。本文以人工内分泌系统为主要研究对象,针对目前人工内分泌系统研究的不足之处,努力挖掘出内分泌系统蕴含的信息处理机制,为自然计算研究带来新的启发。
本文主要从以下四个方面展开工作：
1.通过研究人体内分泌系统与神经系统的相互作用机制,努力挖掘两者之间的相互调节关系,对现有人工神经网络模型进行改进。
在对机体的调节过程中,内分泌系统和神经系统存在紧密的相互作用关系,两者合作共同对机体进行调节。神经系统通过神经内分泌细胞调节内分泌系统的活动。内分泌系统分泌的激素可调节神经元的兴奋性,并可从高层影响神经系统的活动。两者的相互作用机制非常复杂,蕴含丰富的信息处理机制,并体现出一定的自适应、自组织、自学习的特点。因此,神经系统与内分泌系统相互作用的机制值得深入研究。本文在对神经系统与内分泌系统相互作用的生理机制进行深入研究的基础上,提出了一种新的利用人工内分泌系统调节人工神经网络的人工神经内分泌模型。相比传统人工神经网络,该模型可以使人工神经网络动态适应环境变化。
2.研究人体内分泌系统在调节机体维持内稳态中的作目,在其作用机制的启发下,提出协助保持控制系统处于稳定状态的人工内分泌模型。
内分泌系统与神经系统一起相互合作对机体进行调节,其最重要的功能之一就是维持机体的内平衡。人体的内平衡对机体的正常工作具有重要意义,是维持人体生命的基本条件之一。内分泌系统和神经系统一起组成一个负反馈系统,调节内分泌系统的活动,保持人体内环境的稳态。神经系统也可以从高层感知外界环境变化,调节内分泌系统的活动,维持机体的内平衡。受该机制启发,本文提出了协助保持系统处于稳定状态的入工内分泌控制模型。该模型可以协助保持控制系统的状态处于一个相对稳定和平衡的状态,从而更好地起到控制作用。
3.研究激素在细胞间扩散的机制,在该机制的启发下,本文提出一种应用于多机器人系统运动控制的模型。
研究内分泌系统和神经系统相互调节相互作用的机制是从宏观角度对内分泌系统的信息处理机制进行研究。而从微观角度研究,内分泌系统对机体的调节是通过激素以分布式的方式进行的。激素由内分泌腺体分泌,经多种传播方式扩散到机体各处,对机体进行分布式的调节。激素在机体中的传播受激素的浓度、种类等多种因素影响。由于激素传播过程固有的分布性,研究该过程中的信息传递与控制机制,可以给多机器人系统等分布式系统带来新的启发。基于激素在细胞间传递的生理机制,本文提出了应用于多机器人系统运动控制的分布式人工内分泌模型。相比传统类似模型,该模型具有计算量较少、通用性强等优点。
4.在激素传播与作用机制的启发下,研究应用于多机器人系统协同定位问题中的新算法。
激素由内分泌细胞分泌后,扩散到全身,对机体进行分布式的调节,其调节过程带有固有的分布式特性。激素的传播过程和作用方式具有很多经典分布式系统信息传播方式不具有的独特特点,但内分泌系统仍可通过激素对机体进行高效的调节。激素的这些传播特性值得深入研究,以给传统分布式系统中的信息传播方式带来新的启发。在激素的生理特性的启发下,本文提出了应用于多机器人系统协同定位问题的人工内分泌模型。相比于传统协同定位算法,该模型可以有效提高多机器人系统协同定位的准确性。
本文在对内分泌系统生理机制深入研究的基础上,对内分泌系统的不同部分进行了深入的分析和建模。基于内分泌系统不同部分蕴含的有启发性的信息处理机制,本文对传统自然计算模型进行了改进或提出了新的自然计算模型。本文对提出的自然计算模型进行了实验验证,实验结果证实了本文提出的自然计算模型的有效性和合理性。
Nature Inspired Computing seeks to discover and extract useful organizing and processing mechanisms of information from nature and to apply them to computing problems which traditional methods cannot solve. In the achievements of Nature Inspired Computing, algorithms inspired by human body are the most successful, such as Artificial Neural Networks, Genetic Algorithm. Because human is the only intelligent creature in the world, it is straightforward to learn from human body to achieve truly intelligence. Therefore, taking inspiration from human body is the most important research direction of Nature Inspired Computing. The information processing organisms of human body mainly include neural system, genetic system, immune system and endocrine system. These systems cooperate to modulate human body and to maintain its normal working. The systems also exhibit high intelligence and self-adaptability in their modulation process, which is source of imitation for Nature Inspired Computing.
Among the control systems of human body, endocrine system is a crucial one. It plays an essential role in the modulation of growth and metabolism. It cooperates closely with neural system and immune system to maintain homeostasis of human body. Endocrine system modulates human body by a substance called hormone, which spreads everywhere in the body and modules the body in a distributed way. Endocrine system and neural system is closely tied together; they cooperate to module human body. Neural system modulates endocrine system by neuroendocrine cells; while endocrine system influences neural system by hormone. Neural system and endocrine system cooperate to form a negative feedback loop to ensure homeostasis of human body. In endocrine system itself, there are complex negative feedback modulation mechanisms too, which ensure that its modulation of human body is controllable. At present, research on endocrine system of human body is still premature. Summing all of those up, endocrine system must bear some new inspiration to computing theory. Therefore, endocrine system is worthy a lot of research efforts, and is an important research direction of Nature Inspired Computing. Computing models inspired by endocrine system are called Artificial Endocrine Models. Research on Artificial Endocrine Models is still very preliminary and applications of Artificial Endocrine Models are still very limited. Aiming as a supplement for previous work, this thesis will focus on endocrine system and strive to extract meaningful information processing mechanisms from it.
This thesis is mainly composed of four parts:
1. By studying the ties between neural system and endocrine system, this thesis analyzes and formalizes their correlations and applys their interaction mechanisms to improve traditional Artificial Neural Networks.
When controlling human body, neural system cooperates with endocrine system closely. Neural system modulates endocrine system by neuroendocrine cells. On the other hand, hormones can affect excitement of neurons. Moreover, endocrine system can influence emotion, which could modulte neural system from high level. After thorough survey on relations between neural system and endocrine system, this thesis proposes TAES, in which Artificial Endocrine System modulates Artificial Neural Networks. Compared with traditional Artificial Neural Networks, TAES enables Artificial Neural Networks to adapt to dynamical environment more quickly.
2. By studying the way endocrine system maintains homeostasis of human body, this thesis propose a model in which endocrine system help maintain stability of control system.
One of the most important functions of endocrine system and neural system is to maintain homeostasis of human body. Generally, endocrine system cooperates with neural system to maintain homeostasis of human body. Neural system and endocrine system form a negative feedback loop together, which guarantees homeostasis of human body. Such processes are self-adaptive and self-organized, contains a lot of interesting and complex information processing mechanisms. Inspired by such mechanism, this thesis proposes a dynamic control system called EMNCS. Compared with traditional control systems, EMNCS can help maintain stability of the control system.
3. Inspired by biological hormone, this thesis proposes a distributed control model applied in multi-robot system.
To study relation of endocrine system and neural system is to study endocrine system in macroscopic perspective. However, from microscopic perspective, endocrine system modulates human body by hormones. Hormones are secreted by endocrinal glands and spread everywhere in human body via many different transmission channels. The ways hormone spread are correlated with density, type, and many other factors. Due to the distributive characteristics of hormone, research on mechanisms of hormones may give distinct insight to distributed systems like multi-robot system and others. Inspired by hormone, we propose a model called DIIMRCS which is used to control motion of robots in multi-robot system. Experimental results show that DHMRCS can reduce computation substantially.
4. This thesis scrutinizes the ways hormone spreads and functions and formalizes many distinct characteristics of it. Inspired by such characteristics, this thesis proposes an algorithm which is applied in formation problem in multi-robot system.
The mechanisms by which hormones spread are different from common communication principles of modern distributed systems. There hide interesting distributed communication and control ideas in endocrine system. Inspired by hormone's way of communication and control, we propose DHFS, which is applied in formation problem in multi-robot system. Experimental results show that DHFS can improve precision of formation for multi-robot system compared with traditional formation algorithms.
Based on thorough study on biological endocrine system, this thesis analyzes and scrutinizes many aspects of the system. By extracting meaningful mechanisms of the systems and apply them in many computing problems, this thesis either improve traditional Nature Inspired Computing models or propose new computing models. This thesis has made many experiments to verify proposed models, and the experimental results confirm rationality and effectiveness of those models.
在人体的四个主要控制系统中,内分泌系统对人体的生长发育、新陈代谢等功能起到重要的调节作用。内分泌系统与神经系统、免疫系统紧密联系、相互合作,一起维持着机体的内平衡。内分泌系统通过一种称为激素的物质对机体进行调节,激素扩散到全身,对机体进行分布式的调节。内分泌系统与神经系统存在紧密的相互联系,它们互相调节互相作用。神经系统通过神经内分泌细胞调节内分泌系统的活动；内分泌系统则通过激素调节神经系统的活动。内分泌系统分泌的一些激素可以影响人的情感,从高层对神经网络进行调节。神经系统和内分泌系统共同组成一个负反馈调节系统,维持机体内环境的稳态。内分泌系统内部存在着复杂的反馈调节关系,以保证其对机体的调节处于可控状态。目前,对内分泌系统生理机制的研究还不充分,很多内分泌系统的信息作用机制仍未得到充分理解。由此可见,人体的内分泌系统蕴含丰富的信息处理机制,可以给自然计算研究带来新的启发,是自然计算的重要研究方向。受内分泌系统启发提出的自然计算模型称为人工内分泌系统。目前,对内分泌系统生理机制的建模已经蓬勃开展,但是该领域的研究仍不充分,应用研究还不够广泛。本文以人工内分泌系统为主要研究对象,针对目前人工内分泌系统研究的不足之处,努力挖掘出内分泌系统蕴含的信息处理机制,为自然计算研究带来新的启发。
本文主要从以下四个方面展开工作：
1.通过研究人体内分泌系统与神经系统的相互作用机制,努力挖掘两者之间的相互调节关系,对现有人工神经网络模型进行改进。
在对机体的调节过程中,内分泌系统和神经系统存在紧密的相互作用关系,两者合作共同对机体进行调节。神经系统通过神经内分泌细胞调节内分泌系统的活动。内分泌系统分泌的激素可调节神经元的兴奋性,并可从高层影响神经系统的活动。两者的相互作用机制非常复杂,蕴含丰富的信息处理机制,并体现出一定的自适应、自组织、自学习的特点。因此,神经系统与内分泌系统相互作用的机制值得深入研究。本文在对神经系统与内分泌系统相互作用的生理机制进行深入研究的基础上,提出了一种新的利用人工内分泌系统调节人工神经网络的人工神经内分泌模型。相比传统人工神经网络,该模型可以使人工神经网络动态适应环境变化。
2.研究人体内分泌系统在调节机体维持内稳态中的作目,在其作用机制的启发下,提出协助保持控制系统处于稳定状态的人工内分泌模型。
内分泌系统与神经系统一起相互合作对机体进行调节,其最重要的功能之一就是维持机体的内平衡。人体的内平衡对机体的正常工作具有重要意义,是维持人体生命的基本条件之一。内分泌系统和神经系统一起组成一个负反馈系统,调节内分泌系统的活动,保持人体内环境的稳态。神经系统也可以从高层感知外界环境变化,调节内分泌系统的活动,维持机体的内平衡。受该机制启发,本文提出了协助保持系统处于稳定状态的入工内分泌控制模型。该模型可以协助保持控制系统的状态处于一个相对稳定和平衡的状态,从而更好地起到控制作用。
3.研究激素在细胞间扩散的机制,在该机制的启发下,本文提出一种应用于多机器人系统运动控制的模型。
研究内分泌系统和神经系统相互调节相互作用的机制是从宏观角度对内分泌系统的信息处理机制进行研究。而从微观角度研究,内分泌系统对机体的调节是通过激素以分布式的方式进行的。激素由内分泌腺体分泌,经多种传播方式扩散到机体各处,对机体进行分布式的调节。激素在机体中的传播受激素的浓度、种类等多种因素影响。由于激素传播过程固有的分布性,研究该过程中的信息传递与控制机制,可以给多机器人系统等分布式系统带来新的启发。基于激素在细胞间传递的生理机制,本文提出了应用于多机器人系统运动控制的分布式人工内分泌模型。相比传统类似模型,该模型具有计算量较少、通用性强等优点。
4.在激素传播与作用机制的启发下,研究应用于多机器人系统协同定位问题中的新算法。
激素由内分泌细胞分泌后,扩散到全身,对机体进行分布式的调节,其调节过程带有固有的分布式特性。激素的传播过程和作用方式具有很多经典分布式系统信息传播方式不具有的独特特点,但内分泌系统仍可通过激素对机体进行高效的调节。激素的这些传播特性值得深入研究,以给传统分布式系统中的信息传播方式带来新的启发。在激素的生理特性的启发下,本文提出了应用于多机器人系统协同定位问题的人工内分泌模型。相比于传统协同定位算法,该模型可以有效提高多机器人系统协同定位的准确性。
本文在对内分泌系统生理机制深入研究的基础上,对内分泌系统的不同部分进行了深入的分析和建模。基于内分泌系统不同部分蕴含的有启发性的信息处理机制,本文对传统自然计算模型进行了改进或提出了新的自然计算模型。本文对提出的自然计算模型进行了实验验证,实验结果证实了本文提出的自然计算模型的有效性和合理性。
Nature Inspired Computing seeks to discover and extract useful organizing and processing mechanisms of information from nature and to apply them to computing problems which traditional methods cannot solve. In the achievements of Nature Inspired Computing, algorithms inspired by human body are the most successful, such as Artificial Neural Networks, Genetic Algorithm. Because human is the only intelligent creature in the world, it is straightforward to learn from human body to achieve truly intelligence. Therefore, taking inspiration from human body is the most important research direction of Nature Inspired Computing. The information processing organisms of human body mainly include neural system, genetic system, immune system and endocrine system. These systems cooperate to modulate human body and to maintain its normal working. The systems also exhibit high intelligence and self-adaptability in their modulation process, which is source of imitation for Nature Inspired Computing.
Among the control systems of human body, endocrine system is a crucial one. It plays an essential role in the modulation of growth and metabolism. It cooperates closely with neural system and immune system to maintain homeostasis of human body. Endocrine system modulates human body by a substance called hormone, which spreads everywhere in the body and modules the body in a distributed way. Endocrine system and neural system is closely tied together; they cooperate to module human body. Neural system modulates endocrine system by neuroendocrine cells; while endocrine system influences neural system by hormone. Neural system and endocrine system cooperate to form a negative feedback loop to ensure homeostasis of human body. In endocrine system itself, there are complex negative feedback modulation mechanisms too, which ensure that its modulation of human body is controllable. At present, research on endocrine system of human body is still premature. Summing all of those up, endocrine system must bear some new inspiration to computing theory. Therefore, endocrine system is worthy a lot of research efforts, and is an important research direction of Nature Inspired Computing. Computing models inspired by endocrine system are called Artificial Endocrine Models. Research on Artificial Endocrine Models is still very preliminary and applications of Artificial Endocrine Models are still very limited. Aiming as a supplement for previous work, this thesis will focus on endocrine system and strive to extract meaningful information processing mechanisms from it.
This thesis is mainly composed of four parts:
1. By studying the ties between neural system and endocrine system, this thesis analyzes and formalizes their correlations and applys their interaction mechanisms to improve traditional Artificial Neural Networks.
When controlling human body, neural system cooperates with endocrine system closely. Neural system modulates endocrine system by neuroendocrine cells. On the other hand, hormones can affect excitement of neurons. Moreover, endocrine system can influence emotion, which could modulte neural system from high level. After thorough survey on relations between neural system and endocrine system, this thesis proposes TAES, in which Artificial Endocrine System modulates Artificial Neural Networks. Compared with traditional Artificial Neural Networks, TAES enables Artificial Neural Networks to adapt to dynamical environment more quickly.
2. By studying the way endocrine system maintains homeostasis of human body, this thesis propose a model in which endocrine system help maintain stability of control system.
One of the most important functions of endocrine system and neural system is to maintain homeostasis of human body. Generally, endocrine system cooperates with neural system to maintain homeostasis of human body. Neural system and endocrine system form a negative feedback loop together, which guarantees homeostasis of human body. Such processes are self-adaptive and self-organized, contains a lot of interesting and complex information processing mechanisms. Inspired by such mechanism, this thesis proposes a dynamic control system called EMNCS. Compared with traditional control systems, EMNCS can help maintain stability of the control system.
3. Inspired by biological hormone, this thesis proposes a distributed control model applied in multi-robot system.
To study relation of endocrine system and neural system is to study endocrine system in macroscopic perspective. However, from microscopic perspective, endocrine system modulates human body by hormones. Hormones are secreted by endocrinal glands and spread everywhere in human body via many different transmission channels. The ways hormone spread are correlated with density, type, and many other factors. Due to the distributive characteristics of hormone, research on mechanisms of hormones may give distinct insight to distributed systems like multi-robot system and others. Inspired by hormone, we propose a model called DIIMRCS which is used to control motion of robots in multi-robot system. Experimental results show that DHMRCS can reduce computation substantially.
4. This thesis scrutinizes the ways hormone spreads and functions and formalizes many distinct characteristics of it. Inspired by such characteristics, this thesis proposes an algorithm which is applied in formation problem in multi-robot system.
The mechanisms by which hormones spread are different from common communication principles of modern distributed systems. There hide interesting distributed communication and control ideas in endocrine system. Inspired by hormone's way of communication and control, we propose DHFS, which is applied in formation problem in multi-robot system. Experimental results show that DHFS can improve precision of formation for multi-robot system compared with traditional formation algorithms.
Based on thorough study on biological endocrine system, this thesis analyzes and scrutinizes many aspects of the system. By extracting meaningful mechanisms of the systems and apply them in many computing problems, this thesis either improve traditional Nature Inspired Computing models or propose new computing models. This thesis has made many experiments to verify proposed models, and the experimental results confirm rationality and effectiveness of those models.
引文
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